The potential of wind power as a global
source of electricity is assessed by using winds
derived through assimilation of data from a variety
of meteorological sources. The analysis indicates
that a network of land-based 2.5-megawatt (MW) turbines
restricted to nonforested, ice-free, nonurban areas
operating at as little as 20% of their rated capacity
could supply >40 times current worldwide consumption
of electricity, >5 times total global use of energy
in all forms. Resources in the contiguous United
States, specifically in the central plain states,
could accommodate as much as 16 times total current
demand for electricity in the United States. Estimates
are given also for quantities of electricity that
could be obtained by using a network of 3.6-MW turbines
deployed in ocean waters with depths <200 m within
50 nautical miles (92.6 km) of closest coastlines.

Wind power accounted for 42% of all
new electrical capacity added to the United States
electrical system in 2008 although wind continues
to account for a relatively small fraction of the
total electricity-generating capacity [25.4 gigawatts
(GW) of a total of 1,075 GW] (ref. 1; www.awea.org/pubs/documents/Outlook_2009.pdf).
The Global Wind Energy Council projected the possibility
of a 17-fold increase in wind-powered generation
of electricity globally by 2030 (ref. 2; www.gwec.net/fileadmin/documents/Publications/GWEO_2008_final.pdf).
Short et al. (3),
using the National Renewable Energy
Laboratory's WinDs model, concluded that wind could
account for as much as 25% of U.S. electricity by
2050 (corresponding to an installed wind capacity
of ≈300 GW).

Archer and Jacobson
(4) estimated
that 20% of the global total wind power potential
could account for as much as 123 petawatt-hours
(PWh) of electricity annually [corresponding to
annually averaged power production of 14 terawatts
(TW)] equal to 7 times the total current global
consumption of electricity (comparable to present
global use of energy in all forms). Their study
was based on an analysis of data for the year 2000
from 7,753 surface meteorological stations complemented
by data from 446 stations for which vertical soundings
were available. They restricted their attention
to power that could be generated by using a network
of 1.5-megawatt (MW) turbines tapping wind resources
from regions with annually averaged wind speeds
in excess of 6.9 m/s (wind class 3 or better) at
an elevation of 80 m. The meteorological stations
used in their analysis were heavily concentrated
in the United States, Europe, and Southeastern Asia.
Results inferred for other regions of the world
are subject as a consequence to considerable uncertainty.

The present study is based on a simulation
of global wind fields from version 5 of the Goddard
Earth Observing System Data Assimilation System
(GEOS-5 DAS). Winds included in this compilation
were obtained by retrospective analysis of global
meteorological data using a state-of-the-art weather/climate
model incorporating inputs from a wide variety of
observational sources
(5),
including not only surface
and sounding measurements as used by Archer and
Jacobson (4)
but also results from a diverse suite
of measurements and observations from a combination
of aircraft, balloons, ships, buoys, dropsondes
and satellites, in short the gamut of observational
data used to provide the world with the best possible
meteorological forecasts enhanced by application
of these data in a retrospective analysis. The GEOS-5
wind field is currently available for the period
2004 to the present (March 20, 2009) with plans
to extend the analysis 30 years back in time. The
GEOS-5 assimilation was adopted in the present analysis
to take advantage of the relatively high spatial
resolution available with this product as compared
with the lower spatial resolutions available with
alternative products such as ERA-40, NECP II, and
JRA-25. It is used here in a detailed study of the
potential for globally distributed wind-generated
electricity in 2006.

We begin with a description of the
methodology adopted for the present study. The land-based
turbines envisaged here are assumed to have a rated
capacity of 2.5 MW with somewhat larger turbines,
3.6 MW, deployed offshore, reflecting the greater
cost of construction and the economic incentive
to deploy larger turbines to capture the higher
wind speeds available in these regions. In siting
turbines over land, we specifically excluded densely
populated regions and areas occupied by forests
and environments distinguished by permanent snow
and ice cover (notably Greenland and Antarctica).
Turbines located offshore were restricted to water
depths <200 m and to distances within 92.6 km (50
nautical miles) of shore.

These constraints are then discussed,
and results from the global analysis are presented
followed by a more detailed discussion of results
for the United States.

Methodology

The GEOS-5 analysis uses a terrain-following
coordinate system incorporating 72 vertical layers
extending from the surface to a pressure level of
0.01 hPa (an altitude of ≈78.2 km)
(5).
Individual
volume elements are defined by their horizontal
boundaries (latitude and longitude) and the pressures
at their top and bottom. The horizontal resolution
of the simulation is 2/3° longitude by 1/2° latitude
(equivalent to ≈66.7 km × 50.0 km at midlatitudes).
The model provides 3D pressure fields at both layer
centers and layer edges in addition to wind speeds
(meridional and zonal) and temperatures at the midpoint
of individual layers with a time resolution of 6
h. The 3 lowest layers are centered at approximate
altitudes of 71, 201, and 332 m. The 6-h data for
the 3 lowest layers are used in the present analysis
by using an interpolation scheme indicated as follows
to estimate temperatures, pressures, and wind speeds
at 100 m, the hub height for the 2.5- and 3.6-MW
turbines considered here. Knowing pressures at the
lower and upper edges of individual layers together
with temperatures and pressures at the midpoints
of the layers, altitudes corresponding to the midpoints
of the layers are calculated based on an iterative
application of the barometric law by assuming a
linear variation of temperature between the midpoints
of individual layers. The barometric law was also
used to calculate the pressure at 100 m. Wind speeds
and temperatures at 100 m were computed by using
a cubic spline fit to data at the midpoints of the
3 lowest layers.

The kinetic energy of the wind intercepted
by the blades of a turbine per unit time (P) depends
on the density of the air (ρ), the area swept by
the rotor blades (πr2), and the cube of the wind
speed (V3) reduced by an efficiency or power factor
(fp) according to the formula
(6):

The efficiency with which kinetic
energy intercepted at any given wind speed is converted
to electricity by the turbine depends on details
of the turbine design specified by what is referred
to as the turbine power curve. Typically, conversion
to electricity varies as the cube of the wind speed
at low wind speeds, asymptoting to a constant value
for moderate to higher wind speeds, dropping to
0 at the highest wind speeds when the blades of
the turbine are normally feathered to prevent damage.
For the present purpose, we chose to use power curves
and technical parameters for 2.5- and 3.6-MW turbines
marketed by General Electric (GE)
(http://gepower.com/businesses/ge_wind_energy/en/index.htm).

These power curves assume an air density
of 1.225 kg/m3 under conditions corresponding to
an air temperature of 15 °C at a pressure of 1 atmosphere
(7).
To account for the differences in air density
at the rotor elevations as compared with this standard,
wind speeds in the published power/wind speed curves
were adjusted according to the formula

where P and T identify the air pressures
and temperatures at the hub height and R denotes
the atmospheric gas constant, 287.05 N·m/(kg·K)
for dry air.

Optimal spacing of turbines in an
individual wind farm involves a tradeoff among a
number of factors, including the costs of individual
turbines, costs for site development, and costs
for laying power cables, in addition to expenses
anticipated for routine operations and maintenance
(O&M). Turbines must be spaced to minimize interference
in airflow caused by interactions among individual
turbines. This process requires a compromise between
the objective to maximize the power generated per
turbine and the competing incentive to maximize
the number of turbines sited per unit area
(8).
Restricting overall power loss to <20% requires
a downstream spacing of >7 rotor diameters with
cross-wind spacing of >4 diameters (
9,
10). Applying
this constraint to the 2.5-MW GE turbines (rotor
diameter 100 m, r = 50 m) requires an interturbine
areal spacing of 0.28 km2. Similar restrictions
apply to the spacing of offshore turbines (rotor
diameter 111 m, r = 55.5 m). For present purposes
we assume an area for individual offshore turbines
of 5 × 10 rotor diameters corresponding to an occupation
area per turbine of 0.616 km2. The greater spacing
for offshore turbines was selected to ensure that
the overall power loss should be limited to 10%
compensating for the presumed higher cost of installation
and greater O&M expense for turbines operating in
the more hostile marine environment (
8,
9). Subject
to these constraints, we propose to calculate the
electricity that could be generated potentially
every 6 h on the scale of the individual grid elements
defined by the GEOS database (≈66.7 km × 50.0 km)
subject to the additional spatial limitations identified
below.

In addition to providing an estimate
for the maximum potential power generation, we propose
to evaluate also the power yield expressed as a
fraction of the rated power potential of the installed
turbines, i.e., to account for the anticipated variability
of the wind over the course of a year. This quantity
is referred to as the capacity factor (CF), defined
by the relation

where Preal denotes the power actually
realized (neglecting potential interference between
neighboring turbines), and Prated refers to the
power that could have been realized had conditions
permitted the turbine to operate at maximum efficiency
for 100% of the time. We assume in this context
that downtime for maintenance accounts for loss
of only a small fraction of the total potential
power that could be generated by the installed turbines
reflecting the fact that maintenance is normally
scheduled for periods of relatively low wind conditions
(11).
We restrict attention in this analysis to
regions with capacity factors >20%.

Geographic Constraints

The Moderate-Resolution Imaging
Spectroradiometer (MODIS) provides a useful record
of the spatial distribution of different types of
land cover for 2001, with a horizontal resolution
of ≈1 km × 1 km. This record will be used
to exclude from our analysis areas classified as
forested, areas occupied by permanent snow or
ice, areas covered by water, and areas identified
as either developed or urban.

Wind speeds are generally lower
over forested areas, reflecting additional surface
roughness. Consequently, turbines would have to be
raised to a higher level in these environments to
provide an acceptable economic return. Although
it might be reasonable for some regions and some
forest types, we elected for these reasons to
exclude forested areas in the present analysis.

The exclusion of water-covered
areas is more problematic. Wind speeds are generally
higher over water as compared with land. However, it
is more expensive to site turbines in aquatic as
compared with terrestrial environments. Public
pressures in opposition to the former are also
generally more intense, at least in the U.S.

Topographic relief data for both
land and ocean areas were derived from the Global
Digital Elevation Model (GTOPO30) of the Earth
Resources Observation and Science Data Center
of the U.S. Geological Survey. The spatial
resolution of this data source for offshore
environments (bottom topography) is
≈1 km × 1 km
(12).
A number of factors conspire
to limit the development of offshore wind farms.
Aesthetic considerations, for example, have limited
development of wind resources in the near-shore
environment in the U.S. although objections to
near-shore development in Europe appear to have
been less influential. There is a need to also
accommodate requirements for shipping, fishing,
and wildlife reserves and to minimize potential
interference with radio and radar installations.
Accounting for these limitations, Musial and
Butterfield
(13)
and Musial (14),
in a study of
offshore wind power potential for the contiguous
U.S., chose to exclude development of wind farms
within 5 nautical miles (nm) (9.3 km) of shore
and restrict development to 33% of the available
area between 5 and 20 nm (9.3–37 km) offshore,
expanding the potential area available to 67%
between 20 and 50 nm (37–92.6 km).

For purposes of this study,
following Dvorak et al.
(15),
we consider 3
possible regimes for offshore development of
wind power defined by water depths of 0–20,
20–50, and 50–200 m. Somewhat arbitrarily,
we limit potential deployment of wind farms
to distances within 50 nm (92.6 km) of the
nearest shoreline, assuming that 100% of the
area occupied by these waters is available
for development.

Wind Power Potential Worldwide

Approximately 1% of the
total solar energy absorbed by the Earth
is converted to kinetic energy in the
atmosphere, dissipated ultimately by friction
at the Earth's surface
(16,
17).
If we assume
that this energy is dissipated uniformly
over the entire surface area of the Earth
(it is not), this would imply an average
power source for the land area of the
Earth of ≈3.4 × 1014W
equivalent to an annual supply of energy
equal to 10,200 quad [10,800 exajoules
(EJ)], ≈22 times total current
global annual consumption of commercial
energy. Doing the same calculation for the
lower 48 states of the U.S. would indicate
a potential power source of 1.76 ×
1013W corresponding to an annual
yield of 527 quad (555 EJ), some 5.3 times
greater than the total current annual
consumption of commercial energy in all
forms in the U.S. Wind energy is not,
however, uniformly distributed over the
Earth and regional patterns of dissipation
depend not only on the wind source available
in the free troposphere but also on the
frictional properties of the underlying surface.

We focus here on the potential
energy that could be intercepted and converted
to electricity by a globally distributed array
of wind turbines, the distribution and properties
of which were described above. Accounting for
land areas we judge to be inappropriate for
their placement (forested and urban regions
and areas covered either by water or by
permanent ice), the potential power source
is estimated at 2,350 quad (2,470 EJ). The
distribution of potential power for this more
realistic case is illustrated in
Fig. 1. We
restricted attention in this analysis to
turbines that could function with capacity
factors at or >20%.

Global distribution of annual
average onshore wind power potential (W/m2) for 2006
accounting for spatial limitations on placement without
limitations on potential realizable capacity factors.

Results for the potential
electricity that could be generated using
wind on a country-by-country basis are
summarized in
Fig. 2 for onshore (A) and
offshore (B) environments.
Placement of the turbines onshore and
offshore was restricted as discussed
earlier.
Table 1 presents a summary of
results for the 10 countries identified
as the largest national emitters of
CO2. The data included here
refer to national reporting of
CO2 emissions and electricity
consumption for these countries in 2005.
An updated version of the table would
indicate that China is now the world's
largest emitter of CO2,
having surpassed the U.S. in the early
months of 2006. Wind power potential
for the world as a whole and the
contiguous U.S. is summarized in
Table 2.

Annual wind energy potential
onshore and offshore for the world and the contiguous U.S.

The results in
Table 1 indicate that large-scale
development of wind power in China could
allow for close to an 18-fold increase in
electricity supply relative to consumption
reported for 2005. The bulk of this wind
power, 89%, could be derived from
onshore installations. The potential for
wind power in the U.S. is even greater,
23 times larger than current electricity
consumption, the bulk of which, 84%, could
be supplied onshore. Results for the
contiguous U.S. will be discussed in more
detail in the next section.
If the top 10 CO2 emitting
countries were ordered in terms of wind
power potential, Russia would rank number
1, followed by Canada with the U.S. in
the third position. There is an important
difference to be emphasized, however,
between wind power potential in the
abstract and the fraction of the resource
that is likely to be developed when
subjected to realistic economic constraints.
Much of the potential for wind power
in Russia and Canada is located at large
distances from population centers. Given
the inevitably greater expense of
establishing wind farms in remote
locations and potential public opposition
to such initiatives, it would appear
unlikely that these resources will be
developed in the near term. Despite these
limitations, it is clear that
wind power could make a significant
contribution to the demand for electricity
for the majority of the countries listed in
Table 1, in particular for the 4 largest
CO2 emitters, China, the U.S.,
Russia, and Japan. It should be noted,
however, the resource for Japan is largely
confined to the offshore area, 82% of the
national total. To fully exploit these
global resources will require inevitably
significant investment in transmission
systems capable of delivering this power
to regions of high load demand.

The electricity that could
be generated potentially on a global basis
by using wind, displayed as a function of
an assumed capacity factor cutoff on
installed turbines, is presented in
Fig. 3 for onshore (A) and
offshore (B) environments. The
results in
Fig. 3A suggest that total
current global consumption of electricity
could be supplied by wind while restricting
installation of land-based turbines to
regions characterized by most favorable
wind conditions, regions where the turbines
might be expected to function with capacity
factors >53%. If the cutoff capacity
factor were lowered to 36%, the energy
content of electricity generated by using
wind with land-based turbines globally
would be equivalent to total current global
consumption of energy in all forms. Cutoff
capacity factors needed to accommodate
similar objectives with offshore resources
would need to be reduced as indicated in
Fig. 3B. To place these
considerations in context, we would note
that capacity factors realized by turbines
installed in the U.S. in 2004 and 2005 have
averaged close to 36%
(18).

An estimate of the electricity that could
be generated for the contiguous U.S. on a monthly basis
(subject to the siting and capacity limitations noted above)
is illustrated for both onshore and offshore environments in
Fig. 4.
Results presented here were computed by using wind data for
2006. Not surprisingly, the wind power potential for both
environments is greatest in winter, peaking in January,
lowest in summer, with a minimum in August. Onshore
potential for January, according to the results presented
in
Fig. 4, exceeds that for August by a factor of 2.5:
the corresponding ratio computed for offshore locations
is slightly larger, 2.9.

Monthly wind energy potential
for the contiguous U.S. in 2006 with monthly electricity
consumption for the entire U.S.

Fig. 4
includes also monthly data for consumption of electricity
in the U.S. during 2006. Demand for electricity exhibits
a bimodal variation over the course of a year with peaks
in summer and winter, minima in spring and fall. Demand
is greatest in summer during the air-conditioning season.
Summer demand exceeds the minimum in spring/fall demand
typically by between 25% and 35% on a U.S. national basis
depending on whether summers are unusually warm or
relatively mild. The correlation between the monthly
averages of wind power production and electricity
consumption is negative. Very large wind power
penetration can produce excess electricity during
large parts of the year. This situation could allow
options for the conversion of electricity to other
energy forms. Plug-in hybrid electric vehicles, for
example, could take advantage of short-term excesses
in electricity system, while energy-rich chemical
species such as H2 could provide a means
for longer-term storage.

Potential wind-generated electricity
available from onshore facilities on an annually
averaged state-by-state basis is presented in
Fig. 5A. Note the high concentration
of the resource in the central plains region
extending northward from Texas to the Dakotas,
westward to Montana and Wyoming, and eastward
to Minnesota and Iowa. The resource in this region,
as illustrated in
Fig. 5B,
is significantly greater than current local demand.
Important exploitation of this resource will require,
however, significant extension of the existing power
transmission grid. Expansion and upgrading of the
grid will be required in any event to meet
anticipated future growth in electricity demand.
It will be important in planning for this expansion
to recognize from the outset the need to accommodate
contributions of power from regions rich in potential
renewable resources, not only wind but also solar.
The additional costs need not, however, be prohibitive
(ref. 18;
www.nrel.gov/docs/fy08osti/41869.pdf).
The Electric Reliability Council of Texas, the
operator responsible for the bulk of electricity
transmission in Texas, estimates the extra cost
for transmission of up to 4.6 GW of wind-generated
electricity at ≈$180 per kW, ≈10% of
the capital cost for installation of the wind
power-generating equipment
(ref. 19;
www.ercot.com/news/presentations/2006/ATTCH_A_CREZ_Analysis_Report.pdf.).

Annual onshore wind energy
potential on a state-by-state basis for the contiguous U.S.
expressed in TWh (A) and as a ratio with respect
to retail sales in the states (2006) (B). For
example, the potential source for North Dakota exceeds
current total electricity retail sales in that state by
a factor of 360. Data source for total electricity retail
sales was www.eia.doe.gov.

An important issue
relating to the integration of electricity derived
from wind into a grid incorporating contributions
from a variety of sources relates to the challenge
of matching supply with load demand, incorporating
a contribution to supply that is intrinsically
variable both in time and space and subject to
prediction errors. This challenge can be mitigated
to some extent if the variations of wind sources
contributing to an integrated transmission grid
from different regions are largely uncorrelated.
An anomalously high contribution from one region
can be compensated in this case by an anomalously
low contribution from another. To investigate the
significance of this potential compensation, we
examined the covariance of wind resource from 3
specific regions, one in Montana, the second in
Minnesota, and the third in Texas, as indicated in
Fig. 6.
Analysis of 6-h averaged potential wind-generated
supplies of electricity from the 3 regions over
the 4 seasons, winter, spring, summer, and fall,
yielded the results summarized in
Table 3.
Contributions from the 3 regions are essentially
uncorrelated during the winter months (October
through March) with r values of <0.07.
Correlation coefficients (r values),
however, are relatively high in summer (July
through September) with values ranging from 0.28
(Montana versus Texas) to 0.37 (Montana versus
Minnesota) with intermediate values in spring. The
analysis suggests that wind power could make a
relatively reliable contribution to anticipated
base load demand in winter. It may be more
difficult to incorporate wind power resources
into projections of base load demand for other
seasons, particularly for summer.

The GEOS-5 winds used here were
obtained through assimilation of meteorological
data from a variety of sources, in combination
with results from an atmospheric general
circulation model. Transport in the boundary
layer was treated by using 2 different
formalisms, one applied under conditions when
the boundary layer was stable, the other under
conditions when the boundary layer was either
unstable or capped by clouds. The variation of
wind speed with altitude was calculated in the
present study by using a cubic spline fit to
the 3 lowest layers (central heights of 71, 201,
and 332 m) of the GEOS-5 output to estimate
wind speeds at the rotor heights of the turbines
considered here (100 m). Wind speeds so
calculated were used in deriving
all of the results presented above.

The rotors of the turbines modeled
in this study are of sufficient size that as the
blades rotate they traverse significant portions
of the 2 lowest layers of the GEOS-5-simulated
atmosphere. Use of wind speed for a single level
(100 m) must be consequently subject to some
uncertainty. To assess this uncertainty we
explored results derived with an alternate
approach. The power intercepted by the blades
of the rotors passing through the separate layers
was calculated initially on the basis of the
reported average wind speeds for the involved
layers. Adopting a typical value of ≈135 m
for the height of the boundary between the first
2 layers, given a rotor diameter of 100 m as
appropriate for the assumed onshore turbines,
it follows that 99% of the area swept out by the
rotors would intercept air from the first layer,
with only 1% encountered in the second layer.
The power intercepted by the rotors may be
calculated in this case by averaging
appropriately the power intercepted in the
2 layers. Implementing this approach yielded
results that differed typically slightly lower,
by <15% for the onshore results presented
above, by <7% for the offshore results.

The GEOS-5 data had a spatial
resolution of ≈66.7 km × 50.0 km. It is
clear that wind speeds can vary significantly
over distances much smaller than the resolution
of the present model in response to changes in
topography and land cover (affected in both
cases by variations in surface roughness). In
general, we expect the electricity yield computed
with a low-resolution model to underestimate
rather than overestimate what would be calculated
by using a higher-resolution model. The GEOS-5
data are expected to provide a useful
representation of winds on a synoptic scale as
required for example to describe the transport
between adjacent grid elements. They would not
be expected to account for subgrid scale
variations in wind speeds even though
the latter might be expected, at least under
some circumstances, to make a significant
contribution to the potentially available
wind power. To test this hypothesis we explored
the implications of a high-resolution wind atlas
available for an altitude of 100 m for
Minnesota
(20).
Wind speeds indicated by the high-resolution
database are higher than the wind speeds
indicated by GEOS-5, supporting our hypothesis.
The close association of wind speed with surface
land classification implied by the high-resolution
Minnesota wind atlas suggests that land
classification data could provide a useful basis
for at least a preliminary downscaling of the
relatively coarse spatial resolution of the
potential wind resources in the present study.

We elected in this study to
exclude forested, urban, permanently ice covered,
and inland water regions. Given the relatively
coarse spatial resolution of the GEOS-5 database,
it is possible that this approach may have failed
to identify localized environments where wind
resources may be unusually favorable and where
investments in wind power could provide an
acceptable economic return. To explore this
possibility, we developed a global land-based
map of the efficiencies with which turbines
with rotors centered at 100 m might be capable
of converting wind energy to electricity. We
included all land areas with the exception of
regions identified as permanently ice-covered
(notably Greenland and Antarctica). Results,
stated in terms of relevant capacity factors,
are presented in Fig. 7.
Regions with particularly favorable capacity
factors, even though forested, urban, or
occupied by extensive bodies of inland waters,
might be considered as potential additional
targets for development.

Global distribution of onshore
capacity factor (%) for winds at 100 m with exclusion of permanent
snow/ice-covered areas such as Antarctic and Greenland.

It is apparent, for example,
that the low-resolution GEOS-5 record
underestimates the wind resource available
in Spain and Portugal (a consequence most
likely of the complex terrains present in
these regions). Sweden is another example
where wind resources indicated with an
available high-resolution wind atlas
(21)
are significantly higher than those implied
by GEOS-5. The discrepancy in this case may
be attributed to the extensive forest cover
of the region and the a priori decision to
neglect such regions in the present global
study. Assessment of the potential
of mountainous or hilly regions is also
problematic. On average, wind speeds in these
regions may be relatively low. Particularly
favorable conditions may exist, however, on
mountain ridges or in passes through
mountainous regions. The Appalachian mountain
range in the U.S. offers a case in point. In
general the low-resolution results tend to
slightly overestimate wind resources in
regions of flat terrain, while
underestimating the potential for regions
defined by more complex topography.

The analysis in this article
suggests that a network of land-based 2.5-MW
turbines operating at as little as 20% of rated
capacity, confined to nonforested, ice-free
regions would be more than sufficient to
account for total current and anticipated
future global demand for electricity. The
potential for the contiguous U.S. could
amount to >16 times current consumption.
Important additional sources of electricity
could be obtained by deploying wind farms
in near-shore shallow water environments.

An extensive deployment of
wind farms may be considered as introducing
an additional source of atmospheric friction.
For example, if the entire current demand
for electricity in the U.S. were to be
supplied by wind, the sink for kinetic
energy associated with the related turbines
would amount to ≈6% of the sink
caused by surface friction over the entire
contiguous U.S. land area, 11% for the region
identified as most favorable for wind farm
development [the region indicated in red in
Fig. 5A defined by
wind resources >280 terawatt hours (TWh)].
The potential impact of major wind electricity
development on the circulation of the atmosphere
has been investigated in a number of recent
studies (22,
23).
Those studies suggest that high levels of wind
development as contemplated here could result
in significant changes in atmospheric circulation
even in regions remote from locations where the
turbines are deployed. They indicate that global
dissipation of kinetic energy is regulated largely
by physical processes controlling the source
rather than the sink. An increase in friction
caused by the presence of the turbines is likely
to be compensated by a decrease in frictional
dissipation elsewhere. Global average surface
temperatures are not expected to change
significantly although temperatures at higher
latitudes may be expected to decrease to a
modest extent because of a reduction in the
efficiency of meridional heat transport
(offsetting the additional warming anticipated
for this environment caused by the build-up
of greenhouse gases). In ramping up
exploitation of wind resources in the future
it will be important to consider the changes
in wind resources that might result from the
deployment of a large number of turbines, in
addition to changes that might arise as a
result of human-induced climate change, to
more reliably predict the economic return
expected from a specific deployment of
turbines.